Systems and methods for generating bookmark video fingerprints

ABSTRACT

Systems and methods for replacing original media bookmarks of at least a portion of a digital media file with replacement bookmarks is described. A media fingerprint engine detects the location of the original fingerprints associated with the portion of the digital media file and a region analysis algorithm characterizes regions of media file spanning the location of the original bookmarks by data class types. The replacement bookmarks are associated with the data class types and are overwritten or otherwise are substituted for the original bookmarks. The replacement bookmarks then are subjected to a fingerprint matching algorithm that incorporates media timeline and media related metadata.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/725,834 filed May 29, 2015 which is a continuation of U.S. patentapplication Ser. No. 14/251,139 filed Apr. 11, 2014, now U.S. Pat. No.9,070,187 issued Jun. 30, 2015 which is a continuation of U.S. patentapplication Ser. No. 13/572,497 filed Aug. 10, 2012 now U.S. Pat. No.8,737,681 issued May 27, 2014 which is a continuation of U.S. patentapplication Ser. No. 12/180,419 filed Jul. 25, 2008, now U.S. Pat. No.8,265,333 issued Sep. 11, 2012 and which claims priority to U.S.Provisional Application Nos. 60/952,514 filed Jul. 27, 2007 and60/952,528 filed Jul. 27, 2007. This application incorporates byreference in its entirety U.S. Provisional Application No. 60/944,765filed Jun. 18, 2007. All of the aforementioned applications areincorporated by reference herein in their entireties.

FIELD OF THE INVENTION

The field relates to generating video and audio fingerprints.

BACKGROUND OF THE INVENTION

Video fingerprinting is a type of a digital watermark utilized bycontent providers to protect their copyrighted video assets without thebenefit of Digital Rights Management (DRM) or other proactive methods ofproviding security for video content. Video content providers willbroadcast and/or distribute their content through traditional real-timebroadcasting systems, such as satellite or cable television, or as filesor streaming media across the Internet. Viewers can utilize third partydevices, such as a Data Video Recorder (DVR), for example a TiVo, torecord and store broadcasts, while other system exist for savingstreaming media to a file or copying a video file from another networklocation. Peer to peer (P2P) programs exist for enabling decentralizedsharing of files regardless of their source or copyright status. Videofingerprinting has been designed to enable a content provider togenerate a unique fingerprint based on processing the entire broadcastor small portions of it. The fingerprint can then be used by processingsuspect video sources for a fingerprint match between the originalsource and the suspect copy to ascertain occurrences of unauthorizedduplication of the original source content.

Copyright infringement is not the only goal of video fingerprinting assuch fingerprinting methods and algorithms are utilized to reviewwhether source and copied video are different. Fingerprinting systemsand methods that are designed to detect and determine differences invideo and audio characteristics between original or authorized mediacontent and copied or duplicated or otherwise modified media contentoften employ fingerprinting technology. Media files can be defined byinherent characteristics such as frame rates for the video portion ofmedia files and bitrates for the audio portion of media files.Fingerprinting can analyze the differences in video frame rates and/oraudio bitrates from separate media sources and ascertain the extent ofmatching of frame rates and/or bitrates between separate media sources.That is, the fingerprint matching can be defined in terms of a percenthit rate of the original media content fingerprint to the fingerprintfound from a media copy. Such fingerprinting systems and methods oftencannot be readily adapted to accommodate the large modifications to theoriginal media broadcast which typical do not occur when the videosource and/or audio sources are being shared by the content provider,but when the video source is being broadcast may become substantiallymodified.

Bookmarks are frame accurate pointers to a location within a videosource. They are also relational to other bookmarks and carry someadditional meta-data that can be utilized later by various other systemsutilizing the bookmark at a later time. Unlike just a video fingerprint,consider a bookmark which is during a commercial and another bookmarkduring the same commercial later in the video source. Today'sfingerprinting algorithms, designed to combat copyright infringement,would consider the video surrounding these bookmarks identical and theresulting fingerprint would be the same. Additionally by sharing theresulting video fingerprint from today's system there would be no way ofknowing which one came first, the time between bookmarks or how toutilize them on a copy of the video source to regenerate a frameaccurate pointer into the video with associated metadata.

The most public solution at this time is the YouTube videofingerprinting system. This system requires the original content ownerto first allow the YouTube fingerprinting engine to scan all of theirvideo assets. Then the engine scans all video files posted to thewebsite for fingerprint matches to content owned by a content provider.The other most public solution is one being developed by PhilipsElectronics.

All of the current systems require all of the original video sources tobe digested and are not designed to work with, for example, sportingevents as these video sources vary dramatically from region to region.Essentially these systems rely heavily on the idea that the contentowner has a copy of all of the versions of the content that have beendistributed or provided to the public. Technology is making it possiblefor a content provider to syndicate their video content to multiplegeographic locations while enabling each individual broadcaster of thecontent to tailor the final visible result based on their own liking.This means that there can be very large differences in timelines, ascommercial times can vary and graphical overlays, as each broadcastercan have radically different methods of showing sports scores. Otherissues break today's systems as each rebroadcast of a live feed cancause differences in the video source such as brightness, cropping,frames per second and bitrates. Today's systems expect the source fedinto the fingerprinting engine be as close as possible to the sourcebeing shared publicly. Although there are some tolerances built in forcontrast, brightness, bitrates and cropping, the range of accepteddeviations for these data points is small as modifications to thesources are typical cutting out video from the larger video segment ortranscoding the source, neither of which produce the differences whichcan be caused by local broadcasters modification, one or more hardwareretransmissions and a wide variety of set top boxes all with highlyvarying video aspect ratios and digital to analog hardware components.An additional shortcoming of the other solutions is that they attempt togenerate fingerprints for the entire video and as many fingerprints aspossible for smaller sub-segments allowing their comparing systems tofind copies of video even if the resulting copy is only a few secondsfrom a source that could be many hours long. To do this the systems arerequired to generate as many fingerprints as possible based on how thefingerprint engine is configured. Thus there is a tradeoff betweenhaving too many fingerprints and not being able to store them all versusthe smallest segment time the content provider would like to detect.

There is a need for a system and method that goes beyond establishingcopyright infringement occurrences.

SUMMARY OF THE PARTICULAR EMBODIMENTS

Systems and methods for replacing original media bookmarks of at least aportion of a digital media file with replacement bookmarks is described.A media fingerprint engine detects the location of the originalfingerprints associated with the portion of the digital media file and aregion analysis algorithm characterizes regions of media file spanningthe location of the original bookmarks by data class types. Thereplacement bookmarks are associated with the data class types and areoverwritten or otherwise are substituted for the original bookmarks. Thereplacement bookmarks then are subjected to a fingerprint matchingalgorithm that incorporates media timeline and media related metadata.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention are described in detail below withreference to the following drawings.

FIG. 1 schematically and pictographically depicts a flow process used bya content creator for presenting a bookmark listing adjacent to a videowindow within a computer executable media player;

FIG. 2 schematically depicts a system utilized by a content creator toprepare the video and bookmark listing presentable within the computerexecutable media player;

FIG. 3 schematically depicts bookmarks along a segment of video frames;

FIG. 4 schematically depicts a system utilized by a third party topresent a third party created bookmark listing adjacent to the contentcreator video and presentable within the computer executable mediaplayer;

FIG. 5 schematically illustrates a bookmark producing algorithm 300utilized by a third party to place a bookmark list adjacent to a contentcreator video presentable within the computer executable media player;

FIG. 6 schematically illustrates an algorithm to retrieve, incorporate,and utilize bookmarks listed in an adjacent panel to the content creatorvideo presentable within the computer executable media player;

FIG. 7 schematically illustrates an expansion of the Procure and Processsub-algorithm 408 of FIG. 6;

FIG. 8 schematically illustrates an expansion of the Examine forMatching Characteristics sub-algorithm 416 of FIG. 7;

FIG. 9 schematically illustrates an expansion of the Execute RegionAlgorithm of sub-algorithm 432 of FIG. 8; and

FIG. 10 schematically illustrates an expansion of the Select HighConfidence Factor Paths algorithm 570.

DETAILED DESCRIPTION OF THE PARTICULAR EMBODIMENTS

Systems described below provide a method for replacing bookmarks indigital media files. The method for replacing bookmarks includesselecting a portion of a digital media file. The digital media file maybe video, audio-video, audio, graphical, or alphanumerical data files.The portion of the media file may include a video frame group set, anaudio segment, or an audio segment of an audio-video file. To theportion of the media file any original accompanying bookmarks aredetected using a fingerprinting algorithm. Video sections or framegroups and/or any audio regions associated with the original bookmarkare subjected to a region analysis algorithm to determine the presenceof data set types or classes. A replacement bookmark is then applied toselected data sets having relevance to the replacement bookmark. Thereplacement bookmark then overwrites the original bookmark and issubsequently fingerprinted.

Moreover, the systems and methods described herein also provide forgenerating bookmark video fingerprints that strikes a balance betweenthe burden of storing too many fingerprints and having too fewfingerprints with an adequate segment time that the content providerwould prefer for establishing content detection. The systems and methodscomprise a fingerprinting engine, a video timeline, event metadata and afingerprint-matching algorithm. Bookmarks are used to provide anchorpoints or media loci that are utilized by the fingerprinting engine withthe video timeline component for automating a timeline of bookmarks thatprovide a relationship methodology between various anchor points. Oneexample could be in which a bookmark A is found with close to a 100%confidence factor, and a bookmark D is also found with close to a 100%confidence factor. If the time between A and D is stored in thefingerprint of A and/or D, and the matched locations of these result inthe exact amount of time between the bookmarks, the confidence factorsincrease or improve beyond just matching the fingerprint but also inmatching the relativity of their respective locations. Additionally ifthe bookmarks between A and D, such as B and C are found with confidencefactor matches substantially less than 100% but still substantial, say70% for example, then by having A and D's timeline verified, theconfidence of B and C increases substantially as the chances of A and Dbeing a near perfect match and not having B and C also being a nearperfect match is small. The confidence factors may be obtained usingstatistical based software products described below. Using the bookmarksas the starting point for generating the fingerprints, the fingerprintengine can ignore all other areas of the video to establish efficientfingerprint management.

Other systems and methods described herein provide for generatingbookmark video fingerprints that strikes a balance between the burden ofstoring too many fingerprints and having too few fingerprints with anadequate segment time that the content provider would prefer forestablishing content detection. The systems and methods comprise afingerprinting engine, a video timeline, event metadata and afingerprint-matching algorithm. Bookmarks are used to provide anchorpoints or media loci that are utilized by the fingerprinting engine withthe video timeline component for automating a timeline of bookmarks thatprovide a relationship methodology between various anchor points. Usingthe bookmarks as the starting point for generating the fingerprints, thefingerprint engine can ignore all other areas of the video to establishefficient fingerprint management.

A particular embodiment includes a method for generating fingerprintsfor digital media files, and a method for detecting media fileduplicates by examining the fingerprints of original source media fileswith the fingerprints of the media file being examined. The methodincludes sharing timeline and metadata of an original media fileincluding at least one of an original digital media file. The originaldigital media file may include a video digital file and/or an audiodigital file. Thereafter, a region within the original media file, forexample a video region or an audio region, is identified. The methodfurther includes bookmarking the identified region within the originalmedia file, either the video file or the audio file. Detecting includesascertaining whether the bookmarked region is a duplicate of theoriginal media file using the fingerprinting algorithm by comparing theoriginal media file to the bookmarked region of the duplicate mediafile.

Alternate embodiments of the method include detecting a bookmarkedregion by selecting a frame group and/or audio segment of the originalsource, examining for the presence of matchable characteristics in theframe group or audio segment, applying a region algorithm to the framegroup or audio segment to establish a loci of the matchablecharacteristic within regions of frames of the frame group or regions ofthe audio segment, removing repetitive occurrences of the matchablecharacteristics, and defining a path of the matchable characteristicswithin the frame group or audio segment. The defined path may beestablished by seeking the optimum vector pathway between matchablecharacteristics. The optimum vector pathway may be ascertained by matrixanalysis of a 10×10 master blocks of video hashes or data set classesobtained from a minimum of 2 video frames to 10 or more video frames. Bymatchable characteristics it is meant that common features may repeat indifferent video frames, for example, and be matched as to locationwithin a video frame relative to a preceding or subsequent frame so thata given characteristic or data type can be compared for its repetitiveoccurrence, or its diminished appearance or outright disappearance.Alternatively, matchable characteristics may also mean uncommon ordissimilar features possessed by any data type entity whose appearancein a video frame series may be tracked, for example the change in musictypes or change in non-musical sounds.

In yet other particular embodiment, disclosure below describes thatdefining a path of the matchable characteristics within the frame groupor audio segment includes performing a matrix analysis of the data setclasses for ascertaining a high confidence pathway. The matrix analysismay include ascertaining the high confidence pathway between similardata set classes, dissimilar data set classes, and a combination ofsimilar and dissimilar data set classes.

In another particular embodiment a method for detecting and replacingoriginal bookmarks accompanying media files with a new set of bookmarksor replacement bookmarks is described. The method allows substituting oroverwriting media files having original bookmarks with bookmarks createdby entities other than the media file owners. The media files mayinclude video, audio-video, audio, graphic, and text files. Thereplacement bookmarks may be associated with fingerprints to allow ameans to track duplication of the replacement bookmarks.

Other embodiments allow for a method to amend original bookmarksaccompanying the media files or adding additional information tosupplement the original bookmarks accompanying the media files. Digitalmedia files received from alternate digital media suppliers aresubjected to a fingerprint engine to ascertain duplication of bookmarks.The method includes sharing timeline and metadata of an original mediafile including at least one of an original media file. The originalmedia file may include a video digital file and/or an audio digitalfile. Thereafter, a region within the original media file, for example avideo region or an audio region, is identified. The method furtherincludes bookmarking the identified region within the original mediafile, either the video file or the audio file. Detecting includesascertaining whether the bookmarked region is a duplicate of theoriginal media file using the fingerprinting algorithm by comparing theoriginal media file to the bookmarked region of the duplicate mediafile.

Another particular embodiment includes a system for generatingfingerprints for media files, and a system for detecting media fileduplicates by examining the fingerprints of original source media fileswith the fingerprints of the media file being examined. The systemincludes a means for sharing timeline and metadata of an original mediafile including at least one of an original media file. The originalmedia file may include a video digital file and/or an audio digitalfile. Thereafter, a region within the original media file, for example avideo region or an audio region, is identified. The method furtherincludes bookmarking the identified region within the original mediafile, either the video file or the audio file. Detecting includes ameans for ascertaining whether the bookmarked region is a duplicate ofthe original media file using the fingerprinting algorithm by comparingthe original media file to the bookmarked region of the duplicate mediafile.

The described methods above also provide for a computerized method todetect bookmarks and ascertain the level of fingerprint matching betweenoriginal source media files, duplicates or copies of media filescompared with the original source media files, and/or bookmark detectionand fingerprint matching between multiple copies of original sourcemedia files. Alternate embodiments of the computerized methods providefor generating fingerprints and replacing bookmarks in digital files.Other alternate embodiments of the computerized methods described aboveprovide for computer readable media having instructions to performcomputerized methods to detect bookmarks, ascertain the level ofbookmark matching and/or fingerprint matching, and replacing bookmarksin digital files, and digital media files, including the video and audioportions of the digital media files. The computer readable media mayinclude the computer executable instructions memorialized or stored inCompact Disk Read Only Memory (CD-ROM) media, Digital Video Disks (DVD)media, Flash memory media, and within magnetic based media locatedwithin internal and/or external hard drives operable by local and/orremote computer systems. The systems utilized by the methods describedabove and below may include standalone computer systems capable ofconnecting with a local network or a wide area network, including theInternet. The standalone computer systems may be online and incommunication with other standalone computer systems, directly, or via aserver, in any local or wide area network, including the Internet. Yetother embodiments for the computerized methods to detect bookmarks andascertain the level of fingerprint matching between original sourcemedia files, duplicates or copies of media files compared with theoriginal source media files may be conveyed electronically from onecomputer system to another computer system, including directly from thedescribed computer readable media to remote computers on a local or widearea network, including the Internet.

These system and methods described above and below extend beyondenforcing copyright protection, but advantageously provide for thesharing of bookmarks, timelines and metadata that, in any combination,may all be embedded into an array of sharable fingerprints. Unlikestrict video fingerprinting, these systems and methods described belowcan find the same bookmarks within the same or dramatically differentversions of the original video, thereby achieving the balance betweenthe burden of storing too many fingerprints and having too fewfingerprints with an adequate segment times.

Alternate embodiments of the systems and methods described hereinprovide for those circumstances in which large modifications to theoriginal broadcast content is made and subsequently re-broadcast inwhole or in part. Such modifications which cause today's fingerprintmethods to fail are graphic and text overlays, such as during a sportingevent each local channel may overlay their own graphics for the scoreand game time, localized banners above or below the content and set topbox differences such as brightness, contrast and video ratio.Additionally each fingerprint in today's methods stand alone, and assuch they do not utilize a relationship to a timeline or specificmetadata about the event as described by the systems and methods hereinfor the detection and/or utilization of facilitating detection of frameaccurate bookmarks.

Furthermore the fingerprint is used only to detect if video is found tobe copied but do not facilitate detection of frame accurate bookmarks.For copyright issues these additional data sources are not useful as thegoal is to simply detect if the copy being reviewed is from a sourcethat has a similar enough fingerprint to be considered the samematerial.

The particular embodiments include systems and/or methods to generatevideo fingerprints, a timeline and metadata which can be shared withanother system to find the same bookmark locations within an identicalor highly changed version of the same broadcast event and, moreparticularly, to enabling these bookmarks to trigger events betweenclients such as allowing a client to mark two points within a broadcastand share these with another client which has access to the samebroadcast which may have different quality, brightness, cropping, audio,broadcast times, commercials and overall timeline modifications.Additionally disclosure herein relates to how to generate such videofingerprints which utilize event metadata, timeline relationships and aunique fingerprinting algorithm to ensure bookmarks can be foundregardless of typical broadcast modifications between geographicregions, distribution systems and other broadcast differences eventhough the main broadcast content is of the same event.

In accordance with the particular and alternate embodiments, there isprovided a fingerprinting engine, a video timeline, event metadata and afingerprint-matching algorithm. Unlike other fingerprinting and matchingalgorithms, this invention assumes that fingerprints are generated onlyaround bookmarks that have been placed manually or via an automatedsystem into the source video. These bookmarks are used as anchor pointswhich are then utilized by the fingerprinting engine as well as thevideo timeline component for automating a timeline of bookmarks whichprovide a relationship methodology between various anchor points. Byutilizing bookmarks as the starting point for generating fingerprints,the engine can ignore all other areas of the video, as this system isnot intended, unlike with other fingerprinting methods, for copyrightprotection, but for sharing of fingerprints, timelines and metadata withanother user and utilizing these to find the same bookmarks within thesame or dramatically different version of the video broadcast/event.Rather than generating hashes of the entire file or subsets of the fileacross the entire file, this system generates multiple hashes utilizingan algorithm that varies the number of frames before and after thebookmark as well as combinations of sub-blocks of the source video.

The fingerprinting engine is configured to review N number of segmentsthat are generated by taking X number of frames prior to and Y number offrames after a bookmark. Where N can be any number larger than one andthe values of X and Y can be fixed or automatically generated based onanother algorithm. For each frame of each segment, the frame itself issegmented into Z number of regions, each region could be of any shapeand they can overlap. The shapes can be guessed based on algorithmswhich utilize sources such as historical statistical data or fixed suchas 20×20 squares used in a matrix analysis covering the entire frame. Itis possible to provide M number of region generation algorithms to thefingerprint engine at the same time. Then, each segment is analyzed forboth motion between frames and static data such as Object CharacterRecognition (OCR) and Object Graphic Recognition (OGC). Then each regionfor each frame is given a set of values that represent a hash of thescale and vector of motion, general image characteristics, OCR and OGCcomponents. The fingerprint engine now has, for each frame within the Nnumber of segments, Z times M number of regions. Additionally it knowsthe original time stamp of the bookmark, which is being used as theanchor point for the generated segments, as well as the timelinerelationship of this bookmark to other bookmarks in the same broadcast.Combinations of paths from the first frame to the last frame aregenerated based on a filtering system that reduces points on each paththat are considered redundant or highly similar. This step is designedto remove dependencies on such things as frames per second or regions ofthe video that are not updated often. After this, a geographic pathwayalgorithm is used to analyze these paths that are essentially filteredcombinations of regions hash values, and sort them based on best toworst path.

In general, the system and methods provide for generating videofingerprints tied to timelines to generate accurate bookmarks within abroadcast for making it possible to share the fingerprints, timeline andevent meta data with another client such that the same bookmarks can beaccurately found within a different video source of the same event fromwhich they were originally generated from. The systems and methodscomprise an event metadata, for passing data associated with the videosuch as broadcast time and title. For sporting events it can be teamsplaying, location and game id; a source video, for providing the sourceframes to the fingerprint engine; a video timeline, for providing thefingerprint engine information about where the bookmark is locatedrelative to other bookmarks and other known attributes of the sourcevideo such as event start times and commercial breaks; a bookmark, foruse as a starting point for generating the segments specific to thislocation in the source video and are also frame accurate pointers intothe video; a segment, for grouping a set of frames relative to abookmark position; a frame, for enabling the region algorithms toutilize individual frames from within a segment as individual digitalimages; a region algorithm, to utilize information from a single frameor multiple frames to break each frame down into discrete regions. Oneregion algorithm, for example, could be a motion detection algorithmwhich would generate regions specific to movement between frames. Asimple region algorithm could also be simply breaking the frame intosmaller 10×10 pixel squares; a region, for representing a single regionfrom within a frame as generated by a region algorithm; a fingerprintengine, for converting regions into binary representations, such as ahash code, and to generate paths from one region in one frame to anothersuch that a path spans all available frames; a path filtering, forremoving redundancies and low threshold elements of a path such that theresulting path represents a near frame rate independent fingerprintunique to the frames which were processed; a path sorting, for sortingthe paths from strongest fingerprint to weakest, where the strongestrepresents a path which when matched results in the highest confidencelevel the two fingerprints are from the same video bookmark; afingerprint matching, for determining if there is a match from anincoming fingerprint against a known set of fingerprints; and analternative video, for representing an alternative path array which wasgenerated utilizing fingerprints, path filtering and path sorting. Acodec may be used to take full frames from a video source and reducethem as much as possible to rasterized and/ or a vector basedinformation that advantegously provides providing sizable compression todata files. The codecs provide the ability to encode and decode datafiles. That is, the codecs may encode a data stream or signal fortransmission, storage or encryption, and/or decode it for viewing orediting. The codecs are often used in videoconferencing and streamingmedia applications. By recognizing patterns in the source frames, thedata within the source frames can be represented digitally in asubstantially reduced byte-containing file. In a way we are repeatingthis process, by taking the video frames and performing some similarfunctions as would a compression algorithm, such as the lossless MovingPictures Expert Group-4 (MPEG4) codec, both the frame data and the datagenerated by available compression algorithms may be utilized. In analternate embodiment, other non-compression style algorithms may beemployed in those circumstances that do not require high resolutionvideo information, but only requires the information that is sufficientto ascertain whether a fingerprint match exists between any two or moremedia files (for example, an original vs. a copy, and/or a copy vs.another copy), either the video portion of the media file, and/or theaudio portion of the media file.

FIG. 1 schematically and pictographically depicts a flow process 10 usedby a content creator for presenting a bookmark listing 28 adjacent to avideo window 26 within a computer executable media player 22, forexample a Windows Media Player skin 22. A video file in a Windows MediaVideo WMV 14 format is merged with an Advanced Stream Redirector or .ASXfile 18, having for example, a playlist and bookmarks. The .ASX file 18is generally used by servers to store information and media files forstreaming video and audio content over the Internet. Content creators ofaudio video files, for example, utilize the WMV file 14 and .ASX files18 to merge into a two-panel Widows Media Player skin 22 having aclosing and re-sizing bar 24 above a video panel 26 adjacent to abookmarks panel 28. The content creator or publisher manufactures orotherwise has control over the production of the two-panel screen shot22 with publisher selected or created audio-video panel 26 withpublisher-created or publisher-selected bookmarks panel 28. The audiovideo content and the bookmark content appearing within the respectivevideo panel and bookmarks panels 26 and 28 of the media player skin 22are controlled by the content creators or publishers.

FIG. 2 schematically depicts a system 50 utilized by the content creatoror publisher to prepare the video and bookmark listing presentablewithin the computer executable media player skin 22. The system 50includes audio video content obtained from a first source 54 to whichbookmarks 58 are merged for presentation or publication 70 in the playerskin 22 as pictographically illustrated in FIG. 1, that is then uploadedto the Internet 70. Thereafter, computer users or clients 78 downloadsthe content creator pre-made video and pre-made bookmarks forpresentation on the client's 78 computers as computer executable WidowsMedia Player skin 22. The skin 22 is observable in YouTube postings,TiVo recordings, Sling Box recordings. Thus video highlight reels of asporting event or historical event can only be bookmarked by thepublisher or content creator.

Also shown in FIG. 2 is an unproductive arm that prevents independentthird party bookmark creators to merge with publisher-provided and/orcontrolled audio video files. Here alternate video source suppliers 60and 64 who independently manufacture bookmarks X 60 and Y 64 are blockedfrom the merging process pictographically depicted in FIG. 1.Accordingly, the user client 68 cannot receive the bookmark contentsfrom independent alternate video source suppliers and third partybookmark manufactures 60 and 64. Thus bookmark information orcommentaries from third party bookmark makers that might be available inmashups and other files are unavailable for consumption or downloadingthrough the Internet 74.

FIG. 3 schematically depicts bookmarks along a segment 100 of videoframes depicted as tick marks of a horizontal time axis 104.Representative periods depicted in this exemplary segment 100 rangesfrom approximately 3 to 15 seconds. Bookmark begin and end locationsrepresenting the time segments for insertion of information containingbookmarks having content related to a group of video frames isdesignated by arrows 122-136. Audio-video frame groups 140, 142, 144,and 146 are respectively defined by begin and end bookmark segments122-128, 126-134, 130-136, and 124-138 available to third party orindependent bookmark generators for placing information content relatedto the video frame groups 140-146.

FIG. 4 schematically and pictographically depicts a flow process 200utilized by a third party to present a third party-created bookmarklisting adjacent to the content creator video and presentable within thecomputer executable media player. By third party is meant independentbookmark creators operating independently from the publishers, owners orgroups authorized by publishers and owners to generate bookmarks toaudio-video content owned by the publishers or owners. The system 200includes the video WMV file 14 that can be incorporated into the videopanel 26 of the two-panel Windows Media Player skin 22. In this flowprocess the Windows Media Video WMV 14 utilizes a Windows Media Videoplayer fingerprint plug-in 210 that allows independent bookmark creatorsto merge information content bookmarks segments similar to bookmarksegments 122-128, 126-134, 130-136, and 124-138 illustrated in FIG. 3for audio-video frame groups 140, 142, 144, and 146. The fingerprintplug-in 210 allows the independent bookmark creators to create bookmarkcontent separate from the audio-video content owners or publishers andpresent it in a bookmark panel 228 adjacent to the audio-video panel 24appearing in a two-panel Windows Media Player skin 230. The fingerprintplug-in 210 allows independent bookmark creators to place interactablebookmarks and/or data in the bookmark panel 228 for any media file,audio, audio-video, and video presenting in the video frame 26.

FIG. 5 schematically illustrates a bookmark producing-and-fingerprintingalgorithm 300 utilized by a third party or independent bookmark creatorto place an interactable and/or data containing bookmark list adjacentto the content creator video presentable within the computer executablemedia player 22. Bookmark producing-and-fingerprinting algorithm 300begins with decision diamond 304 for securing an answer to the query“Video present?” If negative then video, or audio, or audio video filesare acquired at process block 308 and generation algorithm returns todecision diamond 304. If affirmative, generation algorithm routes todecision diamond 312 seeking an answer to the query “Have bookmarks tofingerprint?” If negative, bookmarks are created or generated at processblock 316. If affirmative, previously generated bookmarks are retrievedfrom storage at process block 318. Newly generated or retrievedbookmarks are then routed to process block 320 for bookmarkfingerprinting to ascertain whether unauthorized duplication occurs whenexamining original content sources from other sources. Thereafter, atprocess block 324, fingerprint data is inserted or associated with thenewly created or retrieved bookmark. The bookmarkproducing-and-fingerprinting algorithm 300 then is completed at processblock 328 in which the created and fingerprinted bookmark ismemorialized in a storage medium. The storage medium to store thefingerprinted bookmarks may include magnetic, optical, hard disc, DVD,CD, or flash memory devices.

FIG. 6 schematically illustrates a fingerprinting usage algorithm 400 toretrieve, incorporate, and utilize bookmarks listed in an adjacent panelto the content creator video presentable within the computer executablemedia player. Fingerprinting usage algorithm 400 begins with retrievingfingerprints from storage medium at process block 404, then continueswith procuring and processing video file at process block 408. Procuringand processing is not limited to video files, but may include any mediafile having audio and audio-visual content. Thereafter, at decisiondiamond 500, an answer is sought to the query “Fingerprint found?” Ifnegative, algorithm 400 re-routes to process block 404. If affirmative,algorithm 400 continues to process block 504 where bookmarks and/ormetadata is added. Thereafter, at process block 508, bookmarks areincorporated into the bookmark panel 228 adjacent to video panel 24 ofWindows Media Player skin 230. If usage interaction is passive, forexample reading by the user, fingerprinting usage algorithm 400 iscompleted. If usage is more active, the fingerprinting usage algorithm400 continues and is completed at process block 512, interact withbookmark. Here the user may left-click, right-click, or otherwise signalsome form of active interaction to the listed bookmark text or linkpresented in the bookmark panel 228 using a computer interaction deviceor touch screen.

FIG. 7 schematically illustrates an expansion of the Procure and Processsub-algorithm 408 of FIG. 6. In general the algorithm 408 describes amethod for detecting a bookmarked region by selecting a frame groupand/or audio segment of the original media source, examining for thepresence of matchable characteristics in the frame group or audiosegment, applying a region algorithm to the frame group or audio segmentto establish a loci of the matchable characteristic within regions offrames of the frame group or regions of the audio segment, removingrepetitive occurrences of the matchable characteristics, and defining anaccurate path of the matchable characteristics within the frame group oraudio segment.

Entering from process block 404, defined video frame groups, and oraudio, or audio-visual segments or frame groups, are selected forbookmarking at process block 412. Thereafter, at process block 416,matching characteristics within the defined video frame groups and/oraudio segments are sought or examined for. Matching characteristics mayinclude music genre, sounds other than music, colored regions of videofiles, pixel intensity of video frames, background regions, foregroundregions, objects indicating motion, etc. Once matching characteristicsare defined, Procure and Process sub-algorithm 408 continues withexecuting region algorithm 432 explained in FIG. 9 below. In general,the region algorithm 432 includes identifying subsets of a video frameand examining for a consistent characteristic or appearance that repeatsin some manner, or changes gradually, and/or terminates suddenly orchanges quickly that is indicative of subject matter occurring within,for example, the audio-video frame groups 140, 142, 144, and 146 towhich bookmarks pertain. This could be the path undertaken betweenframes of a basketball, baseball, or soccer ball, a dancer's movement, afootball player's movement, a car chase scene, or something indicatinghuman-to-human interaction, for example a romance scene. In music audiofiles, the repeating characteristics could be a beat, a stanza, or otheraudio quality that can be defined and tracked. The sound increasessignaling the start of a commercial or the pixel darkening indicatingthat a commercial is imminent or other examples used by the regionalgorithm 432 to access the relevant audio-video frame groups toassociate with a bookmark. Thereafter, the Procure and Processsub-algorithm 408 continues with executing a Path algorithm at processblock 500, followed by reducing redundancies at process block 554 thatfilters out or removes repetitive regions. That is the path filtering orredundancy removal algorithm 554 attempts to reduce redundancies andelements of a path where previous or next elements on the same pathrepresent redundant information. Then, at process block 560, a Path-Sortalgorithm is launched at process block 560. The Path-Sort algorithm Pathsorts path and ranks in each of the paths in order from strongest pathto worst, where the strongest path represents a path which when matchedprovides the highest confidence that the other path being matched weregenerated from the same bookmark 18 of the content owner generated WMV14 file location to those bookmarks presented for alternative videosreceived from alternate sources 60 and 64. Once a set of paths is sortedor selected to follow a discernable or definable characteristic in videoor audio segments, a high confidence path is selected at process block570 utilizing confidence factors determined from a matrix analysis ofdata types ˜, !, $, and * described in FIG. 8 below. The algorithmsdescribed here utilized to obtain confidence factors for eachfingerprint match between the media file sources, either the videoportion and/ or the audio portion, may be obtained from analysis ofdifferent fingerprint data using statistical software products, forexample, SAS, obtained from the SAS Institute, Cary, N.C., USA.Thereafter, at process block 580, the high confidence fingerprints arereadied for storage and/or further processing. This completes theProcure and Process sub-algorithm 408 and exits to decision diamond 500of FIG. 6 or process block 320 of FIG. 5.

FIG. 8 schematically illustrates an expansion of the Examine forMatching Characteristics sub-algorithm 416 of FIG. 7. In general thesub-algorithm 416 describes a method to examine for matchablecharacteristics by generating dataset types for regions within eachframe of a frame group or audio segment, associated the data set typeclasses with location points of the video frame or audio segment, andascertaining possible pathways between data set types between loci ofadjacent frames or audio segment regions.

Entering from process block 412, designated data sets of the videoand/or audio frame sets or groups are characterized to types and denotedwith symbols shown in Generate Data Types algorithm of process block420. The Generate Data Types algorithm can utilize, for example, motiondetection, alpha channel luminescence or optical character recognition(OCR) to identify or discern within a given frame into different regionsand/or groups of data points. For example “˜” may designate motion asdetermined by change in position of an object of interest per frame, “*”designate changes in pixel intensity, “!” may designate changes in videobackground or foreground appearances, and “$” may designate changes incharacteristic music themes or non-musical sounds. An example of thechange in characteristic music themes would be when a rock-and-rolemusic theme is gradually or abruptly replaced with a classic opera musictheme. An example of a change in non-musical sounds would be the roar ofa jet engine being replaced with sounds associated with squawking birdsof a jungle. Thereafter, at process block 424, the classified data setsare associated with location points within a video frame or audioregion. Then, at process block 428, possible path combinations aredefined by examining the changes with a particular or commoncharacteristic between video frames or audio segments. For example,paths may be generated from each ˜, !, *, and $ region to form a chainof regions between similar or dissimilar data types between videoframes. That is, a chain of similar or dissimilar regions can be definedas a path having a vector between data set classes. For example, path “$to $”, path “˜ to ˜”, path “! to !”, and path “* to *”. Conversely,possible path combinations may occur between different data types. Forexample, path “$ to *”, path “˜ to !”, path “! to $”, and path “* to ˜”.Upon selecting the possible path combinations, Examine for MatchingCharacteristics sub-algorithm 416 exits to process block 432.

FIG. 9 schematically illustrates an expansion of the Execute RegionAlgorithm 432 of sub-algorithm 416 of FIG. 8. The Execute RegionAlgorithm 432 allows the determination of an original bookmark locationamong an array of fingerprinting paths. Algorithm 432 in flowchart formdepicts an element diagram that shows how source video is broken downinto an array of fingerprint paths for each bookmark and how these canbe compared to an alternative video source to determine of the originalbookmark location is found within the alternative source. Entering fromprocess block 416, Execute Region Algorithm 432 begins with examiningregions of a video frame, here depicted as Region 1 process block 414and Region 2 process block 444, and any metadata accompanying originalfile WMV 14 are reserved in process block 446. The regions examined neednot be limited to two regions, but as many regions of interest presentin a given video frame or audio region. The source video in the form ofthe audio-video frame groups 140, 142, 144, and 146 to which bookmarksegments 122-128, 126-134, 130-136, and 124-138 illustrated in FIG. 3potentially pertain are broken down into an array of fingerprint pathsfor each bookmark segment and how these can be compared to analternative video sources 60 and 64 to determine whether the originalbookmark 18 created by the content creators of WMV file 14 asillustrated in FIG. 1 is found within the alternative video sources 60and 64. Region algorithms in process blocks 448 and 458 are applied tothe video or audio portions of each frame in frame groups 140, 142, 144,and 146 to determine, for example, the occurrence of character types ˜,*, ! and $ from the first frame X of block 460 to the last Frame Y ofblock 462 of a segment 1 in block 464. This process block 424 throughblock 462 is iterative for all the frames of from segment 1 throughsegment N of block 468, as for example, all the frames contained in therespective frame groups 140, 142, 144, and 146. The occurrence ofcharacter types ˜, *, ! and $ are then associated with bookmark 1 ofprocess block 420 to bookmark N of process block 480. Bookmarks 1through N are then associated with event metadata from block 446 withthe source video and timeline data in process block 490. The ExecuteRegion Algorithm 432 is then completed and exits to process block 500.

FIG. 10 schematically illustrates an expansion of the Select HighConfidence Factor Paths algorithm 570 directed to ascertaining highconfidence pathways and high confidence fingerprint matching of videoand/or audio files from alternate video sources 60 and 64. The highconfidence pathway may between similar matchable characteristics or dataset classes, dissimilar, or a combination of similar and dissimilar dataset classes.

Entering from process block 560, algorithm 570 begins with determining aPath 1-R from alternate video sources 60 and/or 64, a fingerprint engine576, and a primary path or Path-1 at process block 580. The fingerprintengine 576 can utilize any metadata information retrieved from themetadata block 446 depicted in FIG. 9 and associate the metadatainformation with the character data types ˜, !, *, $ or other characterdata types. The Path 1-R from alternate video sources 60 and/or 64 aresubjected to a fingerprint matching algorithm at block 586. Thefingerprint matching algorithm 586 utilizes the set of known paths aswell as other data such as event metadata obtained from event metadata446, to determine that separate, third-party or independently createdbookmarks similar to the bookmark segments 122-128, 126-134, 130-136,and 124-138 depicted in FIG. 3 through which are added or associated viathe Fingerprint plug-in 210 of FIG. 4 with the WMV file 14 or other WMVfiles received from alternate sources 60 and 64 are so associated with ahigh confidence level threshold to acquire the relevancy of the contentowner created bookmarks in the ASX file 18 depicted in FIG. 1. Thefingerprint engine 576 can utilize any information from event metadata446 through to specific regions of the video frames or audio segmentsand fingerprint each bookmark associated with the video frame groups orsegments. After fingerprinting the bookmark segments, algorithm 570continues to process block 578 to remove redundant paths and thenselection for an optimal path at Path Sorting block 584. Thereafter,fingerprints are examined for matching characteristics in FingerprintMatching block 586, and matching results are evaluated at process block588. If match is not found, Algorithm 570 is stopped for that sortedpathway at process block 590. If a high accuracy match is found atprocess block 592 for the selected and sorted pathway, the bookmark isapplied at process block 594 to those segments of Alternate Video andVideo Timeline Data at block 596.

While the particular embodiments have been illustrated and described foracquiring efficient bookmark listings to live and/or archived footagesof video, audio, and other data files, other embodiments may includethat bookmark production may be distributed among a series of remoteclients or user operated workstations that coalesce into a singlebookmark listing located in a panel adjacent to a content creator data,audio, or video file presentable within a computer executable mediaplayer. Accordingly, the scope of the invention is not limited by thedisclosure of the preferred embodiment. Instead, the invention should bedetermined entirely by reference to the claims that follow.

The embodiments of the invention in which an exclusive property orprivilege is claimed are defined as follows:
 1. A method for generatingvideo fingerprints comprising: sharing timeline and metadata of anoriginal digital media file including at least one of a video digitalfile and an audio digital file; identifying a region within the originalmedia file; bookmarking the identified region with a fingerprintingalgorithm; detecting a bookmarked region of a duplicate of the originalmedia file using the fingerprinting algorithm; and comparing thebookmarked region of the original media file to the bookmarked region ofthe duplicate.
 2. The method of claim 1, wherein identifying a region ofthe original digital media file includes determining data types withinthe digital media file.
 3. The method of claim 2, wherein determiningdata types includes at least one of a pixel luminescence value, a regionof pixel luminescence values, an indicator of object motion, a change insound volume, and a change in sound types.
 4. The method of claim 1,wherein detecting a bookmarked region includes selecting a frame group,examining for the presence of matchable characteristics in the framegroup, applying a region algorithm to the frame group, removingrepetitive occurrences of the matchable characteristics, and defining apath of the matchable characteristics within the frame group.
 5. Themethod of claim 4, wherein examining for the presence of matchablecharacteristics in the frame group includes generating data set typeclasses for regions within each frame of the frame group, associatingthe data set type classes with location points of the video frame, andascertaining possible pathways between data set type classes betweenloci of adjacent frames.
 6. The method of claim 5, wherein defining apath of the matchable characteristics within the frame group includesperforming a matrix analysis of the data set classes for ascertaining ahigh confidence pathway.
 7. The method of claim 6, wherein defining apath of the matchable characteristics within the frame group includeselecting the high confidence pathway between similar data set classes.8. The method of claim 6, wherein defining a path of the matchablecharacteristics within the frame group include selecting the highconfidence pathway between dissimilar data set classes.
 9. The method ofclaim 6, wherein defining a path of the matchable characteristics withinthe frame group include selecting the high confidence pathway betweensimilar and dissimilar data set classes.
 10. A method for replacingbookmarks in digital media files comprising: selecting a portion of adigital media file; detecting an original bookmark associated with theportion; ascertaining data set classes within the portion of the digitalmedia file; applying a replacement bookmark to associate with the dataset classes; overwriting the original bookmark with the replacementbookmark; and fingerprinting the replacement bookmark.
 11. The method ofclaim 10, wherein detecting the original bookmark includes using afingerprinting algorithm.
 12. The method of claim 10, whereinascertaining data set classes within the portion of the media fileinclude examining for the presence at least one of a pixel luminescencevalue, a region of pixel luminescence values, an indicator of objectmotion, a change in sound volume, and a change in sound types.
 13. Themethod of claim 10, wherein applying a replacement bookmark to associatewith the data set classes includes determining the matchablecharacteristics present in a video frame group of a media file.
 14. Themethod of claim 13, wherein determining the matchable characteristicspresent in a video frame group includes ascertaining the location of atleast one of the pixel luminescence value, the region of pixelluminescence values, the indicator of object motion, the change in soundvolume, and the change in sound types.
 15. The method of claim 14,wherein determining the matchable characteristics present in the videoframe group includes establishing a vector pathway between matchablecharacteristics.
 16. The method of claim 15, wherein establishing thevector pathway between matchable characteristics include selecting for ahigh confidence pathway determined by matrix analysis of the matchablecharacteristics.
 17. The method of claim 16, wherein selecting for ahigh confidence pathway includes vector pathway between similarmatchable characteristics, dissimilar matchable characteristics, and acombination of similar and dissimilar matchable characteristics.
 18. Asystem for generating video fingerprints comprising: an original digitalmedia file having at least one of a video digital file and an audiodigital file; means for identifying a region within the original mediafile; means for bookmarking the identified region with a fingerprintingalgorithm; means for detecting a bookmarked region of a duplicate of theoriginal digital media file using the fingerprinting algorithm; andmeans for comparing the bookmarked region of the original media file tothe bookmarked region of the duplicate.
 19. The system of claim 18,wherein the means for identifying a region within the original mediafile includes a region analysis algorithm configured to detect matchablecharacteristics comprising at least one of a pixel luminescence value, aregion of pixel luminescence values, an indicator of object motion, achange in sound volume, and a change in sound types.
 20. The system ofclaim 19, wherein the means for comparing the bookmarked region of theoriginal media file to the bookmarked region of the duplicate includesdetermining a vector pathway between the matchable characteristics.